Interface between LLMs and your data
Project description
🗂️ LlamaIndex 🦙
LlamaIndex (GPT Index) is a data framework for your LLM application.
PyPI:
- LlamaIndex: https://pypi.tw.martin98.com/project/llama-index/.
- GPT Index (duplicate): https://pypi.tw.martin98.com/project/gpt-index/.
LlamaIndex.TS (Typescript/Javascript): https://github.com/run-llama/LlamaIndexTS.
Documentation: https://gpt-index.readthedocs.io/.
Twitter: https://twitter.com/llama_index.
Discord: https://discord.gg/dGcwcsnxhU.
Ecosystem
- LlamaHub (community library of data loaders): https://llamahub.ai
- LlamaLab (cutting-edge AGI projects using LlamaIndex): https://github.com/run-llama/llama-lab
🚀 Overview
NOTE: This README is not updated as frequently as the documentation. Please check out the documentation above for the latest updates!
Context
- LLMs are a phenomenonal piece of technology for knowledge generation and reasoning. They are pre-trained on large amounts of publicly available data.
- How do we best augment LLMs with our own private data?
We need a comprehensive toolkit to help perform this data augmentation for LLMs.
Proposed Solution
That's where LlamaIndex comes in. LlamaIndex is a "data framework" to help you build LLM apps. It provides the following tools:
- Offers data connectors to ingest your existing data sources and data formats (APIs, PDFs, docs, SQL, etc.)
- Provides ways to structure your data (indices, graphs) so that this data can be easily used with LLMs.
- Provides an advanced retrieval/query interface over your data: Feed in any LLM input prompt, get back retrieved context and knowledge-augmented output.
- Allows easy integrations with your outer application framework (e.g. with LangChain, Flask, Docker, ChatGPT, anything else).
LlamaIndex provides tools for both beginner users and advanced users. Our high-level API allows beginner users to use LlamaIndex to ingest and query their data in 5 lines of code. Our lower-level APIs allow advanced users to customize and extend any module (data connectors, indices, retrievers, query engines, reranking modules), to fit their needs.
💡 Contributing
Interested in contributing? See our Contribution Guide for more details.
📄 Documentation
Full documentation can be found here: https://gpt-index.readthedocs.io/en/latest/.
Please check it out for the most up-to-date tutorials, how-to guides, references, and other resources!
💻 Example Usage
pip install llama-index
Examples are in the examples folder. Indices are in the indices folder (see list of indices below).
To build a simple vector store index:
import os
os.environ["OPENAI_API_KEY"] = 'YOUR_OPENAI_API_KEY'
from llama_index import VectorStoreIndex, SimpleDirectoryReader
documents = SimpleDirectoryReader('data').load_data()
index = VectorStoreIndex.from_documents(documents)
To query:
query_engine = index.as_query_engine()
query_engine.query("<question_text>?")
By default, data is stored in-memory.
To persist to disk (under ./storage):
index.storage_context.persist()
To reload from disk:
from llama_index import StorageContext, load_index_from_storage
# rebuild storage context
storage_context = StorageContext.from_defaults(persist_dir='./storage')
# load index
index = load_index_from_storage(storage_context)
🔧 Dependencies
The main third-party package requirements are tiktoken, openai, and langchain.
All requirements should be contained within the setup.py file. To run the package locally without building the wheel, simply run pip install -r requirements.txt.
📖 Citation
Reference to cite if you use LlamaIndex in a paper:
@software{Liu_LlamaIndex_2022,
author = {Liu, Jerry},
doi = {10.5281/zenodo.1234},
month = {11},
title = {{LlamaIndex}},
url = {https://github.com/jerryjliu/llama_index},
year = {2022}
}
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